MétaCan
Menu
Back to cohort
Record W1973930568 · doi:10.2196/jmir.1752

Communications Between Volunteers and Health Researchers during Recruitment and Informed Consent: Qualitative Content Analysis of Email Interactions

2011· article· en· W1973930568 on OpenAlexafffundabout
Anne Townsend, Zubin Amarsi, Catherine L. Backman, Susan Cox, Linda Li

Bibliographic record

VenueJournal of Medical Internet Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of British ColumbiaArthritis Research Centre of Canada
FundersCanadian Institutes of Health Research
KeywordsContent analysisInformed consentQualitative researchContent (measure theory)PsychologyMedical educationQualitative analysisInternet privacyMedicineComputer scienceAlternative medicineSociologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: While use of the Internet is increasingly widespread in research, little is known about the role of routine electronic mail (email) correspondence during recruitment and early volunteer-researcher interactions. To gain insight into the standpoint of volunteers we analyzed email communications in an early rheumatoid arthritis qualitative interview study. OBJECTIVES: The objectives of our study were (1) to understand the perspectives and motivations of individuals who volunteered for an interview study about the experiences of early rheumatoid arthritis, and (2) to investigate the role of emails in volunteer-researcher interactions during recruitment. METHODS: Between December 2007 and December 2008 we recruited 38 individuals with early rheumatoid arthritis through rheumatologist and family physician offices, arthritis Internet sites, and the Arthritis Research Centre of Canada for a (face-to-face) qualitative interview study. Interested individuals were invited to contact us via email or telephone. In this paper, we report on email communications from 12 of 29 volunteers who used email as their primary communication mode. RESULTS: Emails offered insights into the perspective of study volunteers. They provided evidence prospectively about recruitment and informed consent in the context of early rheumatoid arthritis. First, some individuals anticipated that participating would have mutual benefits, for themselves and the research, suggesting a reciprocal quality to volunteering. Second, volunteering for the study was strongly motivated by a need to access health services and was both a help-seeking and self-managing strategy. Third, volunteers expressed ambivalence around participation, such as how far participating would benefit them, versus more general benefits for research. Fourth, practical difficulties of negotiating symptom impact, medical appointments, and research tasks were revealed. We also reflect on how emails documented volunteer-researcher interactions, illustrating typically undocumented researcher work during recruitment. CONCLUSIONS: Emails can be key forms of data. They provide richly contextual prospective records of an underresearched dimension of the research process: routine volunteer-researcher interactions during recruitment. Emails record the context of volunteering, and the motivations and priorities of volunteers. They also highlight the "invisible work" of research workers during what are typically considered to be standard administrative tasks. Further research is needed to fully understand the role of routine emails, what they may reveal about volunteers' decisions to participate, and their implications for research relationships-for example, whether they have the potential to foster rapport, trust, and understanding between volunteer and researcher, and ultimately shift the power dynamic of the volunteer-researcher relationship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.146
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.146
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0090.013
Scholarly communication0.0050.005
Open science0.0030.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.928
GPT teacher head0.696
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Medical Internet ResearchSame topicSocial Media in Health EducationFrench-language works237,207